Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magnetosphere has space weather consequences such as geomagnetic storms. Accurately predicting the solar wind through measurements of the spatiotemporally evolving conditions in the solar atmosphere is important but remains an unsolved problem in heliophysics and space weather research. In this work, we use deep learning for prediction of solar wind (SW) properties. We use extreme ultraviolet images of the solar corona from space‐based observations to predict the SW speed from the National Aeronautics and Space Administration (NASA) OMNIWEB data set, measured at Lagragian Point 1. We evaluate our model against autoregressive and naive models and find that our model outperforms the benchmark models, obtaining a best fit correlation of 0.55 ± 0.03 with the observed data. Upon visualization and investigation of how the model uses data to make predictions, we find higher activation at the coronal holes for fast wind prediction (≈3 to 4 days prior to prediction), and at the active regions for slow wind prediction. These trends bear an uncanny similarity to the influence of regions potentially being the sources of fast and slow wind, as reported in literature. This suggests that our model was able to learn some of the salient associations between coronal and solar wind structure without built‐in physics knowledge. Such an approach may help us discover hitherto unknown relationships in heliophysics data sets.

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使用深度学习进行太阳风预测

太阳风从太阳日冕的底部散发出来，在行星际介质中充满了带电的带电粒子流，这些带电粒子与地球磁层的相互作用会产生诸如地磁风暴等太空天气后果。通过测量太阳大气中时空演化条件来准确预测太阳风很重要，但在太阳物理学和太空天气研究中仍然是一个未解决的问题。在这项工作中，我们使用深度学习来预测太阳风（SW）的性质。我们使用来自太空观测的太阳日冕的极端紫外线图像来预测美国国家航空航天局（NASA）OMNIWEB数据集的SW速度，该数据集是在Lagragian点1上测得的。我们针对自回归模型和朴素模型评估了我们的模型，发现我们的模型优于基准模型，与观测数据的最佳拟合相关性为0.55±0.03。通过可视化和调查模型如何使用数据进行预测，我们发现在冠状孔处有更高的激活率以进行快速风速预测（预测前约3至4天），并在活动区域​​获得较高的激活率以进行慢速风速预测。正如文献报道的那样，这些趋势与可能是快风和慢风来源的地区的影响有着不可思议的相似性。这表明我们的模型无需内置物理知识即可了解日冕和太阳风结构之间的一些显着联系。这种方法可以帮助我们发现太阳物理学数据集中迄今为止未知的关系。